Journal ArticleDOI
Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition
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TLDR
This paper proposes to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition that outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).About:
This article is published in Pattern Recognition.The article was published on 2013-04-01. It has received 160 citations till now. The article focuses on the topics: Three-dimensional face recognition & Face detection.read more
Citations
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Proceedings ArticleDOI
Fusing the original and its mirror image to perform collaborative representation for face recognition
Min Du,Lianhong Wang,Ziyao Li +2 more
TL;DR: For the small size problem, an adaptive weight selection method is proposed to fuse the original face image and its mirror image based on assigning a better weight to theoriginal face image.
Journal ArticleDOI
Training Set Enlargement Using Binary Weighted Interpolation Maps for the Single Sample per Person Problem in Face Recognition
Yonggeol Lee,Sang-Il Choi +1 more
TL;DR: A method that analyzes the changes in pixels in face images associated with variations by extracting the binary weighted interpolation map (B-WIM) from neutral and variational images in the auxiliary set and creates a new variational image for the query image.
Proceedings ArticleDOI
Multiple collaborative representations for face recognition
TL;DR: A novel representation which integrates original and its virtual face image to represent test sample is proposed which can enlarge the number of training samples for each subject and adequately exploit the detail features of each target image so as to improve the recognition accuracy.
Journal ArticleDOI
Application of improved virtual sample and sparse representation in face recognition
TL;DR: Zhang et al. as mentioned in this paper used an improved non-linear image representation method to highlight the low-intensity and high-intensity pixels of the original training sample, thus generating a virtual sample.
Journal ArticleDOI
Analyzing the Scientific Evolution of Face Recognition Research and Its Prominent Subfields
TL;DR: This paper presents a science mapping approach to analyze thematic evolution of face recognition research, using different bibliometric tools to identify the most important, productive and the highest-impact subfields.
References
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Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
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Two-dimensional PCA: a new approach to appearance-based face representation and recognition
TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.
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Face recognition: a convolutional neural-network approach
TL;DR: A hybrid neural-network for human face recognition which compares favourably with other methods and analyzes the computational complexity and discusses how new classes could be added to the trained recognizer.
Proceedings ArticleDOI
Sparse representation or collaborative representation: Which helps face recognition?
TL;DR: This paper indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification, and proposes a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS).